@article{poursaeed2017vision,
title = {Vision-based Real Estate Price Estimation},
author = {Omid Poursaeed and Tomas Matera and Serge Belongie},
url = {https://vision.cornell.edu/se3/wp-content/uploads/2018/04/Vision-based-Real-Estate-Price-Estimation.pdf},
year = {2018},
date = {2018-03-20},
journal = {Machine Vision and Applications},
abstract = {Since the advent of online real estate database companies like Zillow, Trulia and Redﬁn, the problem of automatic estimation of market values for houses has received considerable attention. Several real estate websites provide such estimates using a proprietary formula. Although these estimates are often close to the actual sale prices, in some cases they are highly inaccurate. One of the key factors that affects the value of a house is its interior and exterior appearance, which is not considered in calculating automatic value estimates. In this paper, we evaluate the impact of visual characteristics of a house on its market value. Using deep convolutional neural networks on a large dataset of photos of home interiors and exteriors, we develop a method for estimating the luxury level of real estate photos. We also develop a novel framework for automated value assessment using the above photos in addition to home characteristics including size, offered price and number of bedrooms. Finally, by applying our proposed method for price estimation to a new dataset of real estate photos and metadata, we show that it outperforms Zillow’s estimates.},
keywords = {}
}

Since the advent of online real estate database companies like Zillow, Trulia and Redﬁn, the problem of automatic estimation of market values for houses has received considerable attention. Several real estate websites provide such estimates using a proprietary formula. Although these estimates are often close to the actual sale prices, in some cases they are highly inaccurate. One of the key factors that affects the value of a house is its interior and exterior appearance, which is not considered in calculating automatic value estimates. In this paper, we evaluate the impact of visual characteristics of a house on its market value. Using deep convolutional neural networks on a large dataset of photos of home interiors and exteriors, we develop a method for estimating the luxury level of real estate photos. We also develop a novel framework for automated value assessment using the above photos in addition to home characteristics including size, offered price and number of bedrooms. Finally, by applying our proposed method for price estimation to a new dataset of real estate photos and metadata, we show that it outperforms Zillow’s estimates.

2017

@techreport{Mendez2017,
title = {Smart Kitchen: A Multi-Modal AR System},
author = {Andrew Mendez and Zexi Liu and Eric Jui-Chun Chien and Serge Belongie},
url = {https://vision.cornell.edu/se3/wp-content/uploads/2017/12/AR-Smart-Kitchen-1.pdf},
year = {2017},
date = {2017-12-15},
abstract = {Augmented Reality(AR) is a novel technology that will revolutionize how we work, learn, and play. AR utilizes computer vision, computer graphics, spatial and tangible interaction to augment our perception and understanding of the environment. As academia and industry are improving computer vision and computer graphics methods to facilitate better AR use, current display technologies, such as mobile and projector-camera (Pro-Cam) hinder its widespread adoption and usefulness due to considerable usability challenges. We propose a novel multi-modal AR system that combines both mobile and pro-cam display technologies. By combining mobile and pro-cam systems will not only remove each display’s limitations, but complement each other’s usability strengths to provide longer, higher-fidelity AR usage. We apply this contribution in the form of a smart kitchen application, that allows users to be provided instant cooking recommendations and intuitive instructions to prepare dishes. We evaluate our system on several participants, and discuss the potential this system brings for widespread adoption of AR.},
keywords = {}
}

Augmented Reality(AR) is a novel technology that will revolutionize how we work, learn, and play. AR utilizes computer vision, computer graphics, spatial and tangible interaction to augment our perception and understanding of the environment. As academia and industry are improving computer vision and computer graphics methods to facilitate better AR use, current display technologies, such as mobile and projector-camera (Pro-Cam) hinder its widespread adoption and usefulness due to considerable usability challenges. We propose a novel multi-modal AR system that combines both mobile and pro-cam display technologies. By combining mobile and pro-cam systems will not only remove each display’s limitations, but complement each other’s usability strengths to provide longer, higher-fidelity AR usage. We apply this contribution in the form of a smart kitchen application, that allows users to be provided instant cooking recommendations and intuitive instructions to prepare dishes. We evaluate our system on several participants, and discuss the potential this system brings for widespread adoption of AR.

2013

@inproceedings{455,
title = {Relative Ranking of Facial Attractiveness},
author = {Hani Altwaijry and Serge Belongie},
url = {/se3/wp-content/uploads/2014/09/043-wacv.pdf},
year = {2013},
date = {2013-01-01},
booktitle = {Workshop on the Applications of Computer Vision (WACV)},
address = {Clearwater Beach, Florida},
abstract = {Automatic evaluation of human facial attractiveness is a challenging problem that has received relatively little attention from the computer vision community. Previous work in this area had posed attractiveness as a classification problem. However, for applications that require fine-grained relationships between objects, learning to rank has been shown to be superior over the direct interpretation of classifier scores as ranks [27]. In this paper, we propose and implement a personalized relative beauty ranking system. Given training data of faces sorted based on a subjecttextquoterights personal taste, we learn how to rank novel faces according to that persontextquoterights taste. Using a blend of Facial Geometric Relations, HOG, GIST, L*a*b* Color Histograms, and Dense-SIFT + PCA feature types, our system achieves an average accuracy of 63% on pairwise comparisons of novel test faces. We examine the effectiveness of our method through lesion testing and find that the most effective feature types for predicting beauty preferences are HOG, GIST, and Dense-SIFT + PCA features.},
keywords = {}
}

Automatic evaluation of human facial attractiveness is a challenging problem that has received relatively little attention from the computer vision community. Previous work in this area had posed attractiveness as a classification problem. However, for applications that require fine-grained relationships between objects, learning to rank has been shown to be superior over the direct interpretation of classifier scores as ranks [27]. In this paper, we propose and implement a personalized relative beauty ranking system. Given training data of faces sorted based on a subjecttextquoterights personal taste, we learn how to rank novel faces according to that persontextquoterights taste. Using a blend of Facial Geometric Relations, HOG, GIST, L*a*b* Color Histograms, and Dense-SIFT + PCA feature types, our system achieves an average accuracy of 63% on pairwise comparisons of novel test faces. We examine the effectiveness of our method through lesion testing and find that the most effective feature types for predicting beauty preferences are HOG, GIST, and Dense-SIFT + PCA features.

2012

@inproceedings{436,
title = {Non-Rigid Surface Detection for Gestural Interaction with Applicable Surfaces},
author = {Andrew Ziegler and Serge Belongie},
url = {/se3/wp-content/uploads/2014/09/0115.pdf},
year = {2012},
date = {2012-01-01},
booktitle = {Applications of Computer Vision (WACV)},
address = {Breckenridge, CO},
abstract = {In this work we present a novel application of non-rigid surface detection to enable gestural interaction with applicable surfaces. This method can add interactivity to traditionally passive media such as books, newspapers, restaurant menus, or anything else printed on paper. We allow a user to interact with these surfaces in a natural manner and present basic gestures based on pointing and touching. This technique was developed as part of an ongoing effort to create an assisted reading device for the visually impaired. However, it is suited to general applications and can be used as a practical mechanism for interaction with screen-less wearable devices. Our key contributions are a unique application of non-rigid surface detection, a basic gesturing paradigm, and a proof of concept system.},
keywords = {}
}

In this work we present a novel application of non-rigid surface detection to enable gestural interaction with applicable surfaces. This method can add interactivity to traditionally passive media such as books, newspapers, restaurant menus, or anything else printed on paper. We allow a user to interact with these surfaces in a natural manner and present basic gestures based on pointing and touching. This technique was developed as part of an ongoing effort to create an assisted reading device for the visually impaired. However, it is suited to general applications and can be used as a practical mechanism for interaction with screen-less wearable devices. Our key contributions are a unique application of non-rigid surface detection, a basic gesturing paradigm, and a proof of concept system.